
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


lowercase    :   Any						=			logging.get_logger(__name__)




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     int		,    _lowerCamelCase					:     Any)   ->       List[Any]:






							'''simple docstring'''





							__UpperCamelCase      :  Dict       =   b.T
							__UpperCamelCase      :  Dict       =   np.sum(np.square(_lowerCamelCase)		,    axis=1)
							__UpperCamelCase      :  Any       =   np.sum(np.square(_lowerCamelCase)		,    axis=0)
							__UpperCamelCase      :  List[Any]       =   np.matmul(_lowerCamelCase		,    _lowerCamelCase)
							__UpperCamelCase      :  Optional[int]       =   aa[:, None] - 2 * ab + ba[None, :]
							return d




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     str		,    _lowerCamelCase					:     Optional[int])   ->       Tuple:






							'''simple docstring'''





							__UpperCamelCase      :  Any       =   x.reshape(-1		,    3)
							__UpperCamelCase      :  int       =   squared_euclidean_distance(_lowerCamelCase		,    _lowerCamelCase)
							return np.argmin(_lowerCamelCase		,    axis=1)




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	_A								=	['pixel_values']







	def __init__(   self						:str	,  a						:Optional[Union[List[List[int]], np.ndarray]] = None	,  a						:bool = True	,  a						:Dict[str, int] = None	,  a						:PILImageResampling = PILImageResampling.BILINEAR	,  a						:bool = True	,  a						:bool = True	,  **a						:Optional[int]	,  )						->					None:
								super().__init__(**a		)
								__UpperCamelCase      :  str       =   size if size is not None else {"height": 2_5_6, "width": 2_5_6}
								__UpperCamelCase      :  List[Any]       =   get_size_dict(a		)
								__UpperCamelCase      :  Optional[int]       =   np.array(a		) if clusters is not None else None
								__UpperCamelCase      :  Optional[int]       =   do_resize
								__UpperCamelCase      :  str       =   size
								__UpperCamelCase      :  List[Any]       =   resample
								__UpperCamelCase      :  Optional[int]       =   do_normalize
								__UpperCamelCase      :  str       =   do_color_quantize







	def   _lowerCamelCase							(   self						:List[str]	,  a						:np.ndarray	,  a						:Dict[str, int]	,  a						:PILImageResampling = PILImageResampling.BILINEAR	,  a						:Optional[Union[str, ChannelDimension]] = None	,  **a						:Optional[int]	,  )						->					np.ndarray:
								__UpperCamelCase      :  Tuple       =   get_size_dict(a		)
								if "height" not in size or "width" not in size:
															raise ValueError(f'Size dictionary must contain both height and width keys. Got {size.keys()}'		)
								return resize(
								    a	,  size=(size["height"], size["width"])	,  resample=a	,  data_format=a	,  **a		)







	def   _lowerCamelCase							(   self						:Union[str, Any]	,  a						:np.ndarray	,  a						:Optional[Union[str, ChannelDimension]] = None	,  )						->					np.ndarray:
								__UpperCamelCase      :  Tuple       =   rescale(image=a	,  scale=1 / 127.5	,  data_format=a		)
								__UpperCamelCase      :  List[str]       =   image - 1
								return image







	def   _lowerCamelCase							(   self						:Dict	,  a						:ImageInput	,  a						:bool = None	,  a						:Dict[str, int] = None	,  a						:PILImageResampling = None	,  a						:bool = None	,  a						:Optional[bool] = None	,  a						:Optional[Union[List[List[int]], np.ndarray]] = None	,  a						:Optional[Union[str, TensorType]] = None	,  a						:Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST	,  **a						:Optional[int]	,  )						->					PIL.Image.Image:
								__UpperCamelCase      :  Dict       =   do_resize if do_resize is not None else self.do_resize
								__UpperCamelCase      :  int       =   size if size is not None else self.size
								__UpperCamelCase      :  str       =   get_size_dict(a		)
								__UpperCamelCase      :  Any       =   resample if resample is not None else self.resample
								__UpperCamelCase      :  int       =   do_normalize if do_normalize is not None else self.do_normalize
								__UpperCamelCase      :  Optional[int]       =   do_color_quantize if do_color_quantize is not None else self.do_color_quantize
								__UpperCamelCase      :  Dict       =   clusters if clusters is not None else self.clusters
								__UpperCamelCase      :  Union[str, Any]       =   np.array(a		)

								__UpperCamelCase      :  Union[str, Any]       =   make_list_of_images(a		)

								if not valid_images(a		):
															raise ValueError(
															    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
															    "torch.Tensor, tf.Tensor or jax.ndarray."		)

								if do_resize and size is None or resample is None:
															raise ValueError("Size and resample must be specified if do_resize is True."		)

								if do_color_quantize and clusters is None:
															raise ValueError("Clusters must be specified if do_color_quantize is True."		)

								# All transformations expect numpy arrays.
								__UpperCamelCase      :  List[Any]       =   [to_numpy_array(a		) for image in images]

								if do_resize:
															__UpperCamelCase      :  Union[str, Any]       =   [self.resize(image=a	,  size=a	,  resample=a		) for image in images]

								if do_normalize:
															__UpperCamelCase      :  Any       =   [self.normalize(image=a		) for image in images]

								if do_color_quantize:
															__UpperCamelCase      :  Any       =   [to_channel_dimension_format(a	,  ChannelDimension.LAST		) for image in images]
															# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
															__UpperCamelCase      :  Dict       =   np.array(a		)
															__UpperCamelCase      :  Any       =   color_quantize(a	,  a		).reshape(images.shape[:-1]		)

															# flatten to (batch_size, height*width)
															__UpperCamelCase      :  Tuple       =   images.shape[0]
															__UpperCamelCase      :  Any       =   images.reshape(a	,  -1		)

															# We need to convert back to a list of images to keep consistent behaviour across processors.
															__UpperCamelCase      :  Dict       =   list(a		)
								else:
															__UpperCamelCase      :  Any       =   [to_channel_dimension_format(a	,  a		) for image in images]

								__UpperCamelCase      :  Any       =   {"input_ids": images}
								return BatchFeature(data=a	,  tensor_type=a		)

import math




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     int)   ->       str:






							'''simple docstring'''





							__UpperCamelCase      :  Union[str, Any]       =   0
							__UpperCamelCase      :  int       =   0
							while num > 0:
														__UpperCamelCase      :  List[Any]       =   num % 8
														__UpperCamelCase      :  Tuple       =   octal + (remainder * math.floor(math.pow(10		,    _lowerCamelCase)))
														counter += 1
														__UpperCamelCase      :  Optional[Any]       =   math.floor(num / 8)  # basically /= 8 without remainder if any
														# This formatting removes trailing '.0' from `octal`.
							return F'0o{int(_lowerCamelCase)}'




def       _SCREAMING_SNAKE_CASE   (						)   ->       None:






							'''simple docstring'''





							print("\n2 in octal is:")
							print(decimal_to_octal(2))  # = 2
							print("\n8 in octal is:")
							print(decimal_to_octal(8))  # = 10
							print("\n65 in octal is:")
							print(decimal_to_octal(65))  # = 101
							print("\n216 in octal is:")
							print(decimal_to_octal(216))  # = 330
							print("\n512 in octal is:")
							print(decimal_to_octal(512))  # = 1000
							print("\n")


if __name__ == "__main__":
		main()